Higher-order spectral analysis of spontaneous speech signals in Alzheimer’s disease

An early and accurate diagnosis of Alzheimer’s disease (AD) has been progressively attracting more attention in recent years. One of the main problems of AD is the loss of language skills. This paper presents a computational framework for classifying AD patients compared to healthy control subjects using information from spontaneous speech signals. Spontaneous speech data are obtained from 30 AD patients and 30 healthy controls. Because of the nonlinear and dynamic nature of speech signals, higher order spectral features (specifically bispectrum) were used for analysis. Four classifiers (k-Nearest Neighbor, Support Vector Machine, Naïve Bayes and Decision tree) were used to classify subjects into three different levels of AD and healthy group based on their performance in terms of the HOS-based features. Ten-fold cross-validation method was used to test the reliability of the classifier results. The results showed that the proposed method had a good potential in AD diagnosis. The proposed method was also able to diagnose the earliest stage of AD with high accuracy. The method has the great advantage of being non-invasive, cost-effective, and associated with no side effects. Therefore, the proposed method can be a spontaneous speech directed test for pre-clinical evaluation of AD diagnosis.     

Relative Accuracy Evaluation

The quality of data plays an important role in business analysis and decision making, and data accuracy is an important aspect in data quality. Thus one necessary task for data quality management is to evaluate the accuracy of the data. And in order to solve the problem that the accuracy of the whole data set is low while a useful part may be high, it is also necessary to evaluate the accuracy of the query results, called relative accuracy. However, as far as we know, neither measure nor effective methods for the accuracy evaluation methods are proposed. Motivated by this, for relative accuracy evaluation, we propose a systematic method. We design a relative accuracy evaluation framework for relational databases based on a new metric to measure the accuracy using statistics. We apply the methods to evaluate the precision and recall of basic queries, which show the result''s relative accuracy. We also propose the method to handle data update and to improve accuracy evaluation using functional dependencies. Extensive experimental results show the effectiveness and efficiency of our proposed framework and algorithms.     

一种用模糊综合评判诊断肝性脑病的方法

本文给出了利用模糊综合评判对脑反应延迟信号分类诊断肝性脑病的新方法．该方法根据连续反应时间原理,用检测对照组和肝性脑病脑反应时间所得的统计特征值构成岭形分布隶属度函数,并用它们构成评判矩阵,然后用已有的统计资料和专家经验对其加权,形成综合评判结果.为说明该方法的使用,文中给出了计算过程,给出的22例表明了该方法的正确性．     

Two-Stage Feature Selection of Voice Parameters for Early Alzheimer's Disease Prediction

Background

The goal of this work is to develop a non-invasive method in order to help detecting Alzheimer's disease in its early stages, by implementing voice analysis techniques based on machine learning algorithms.

Forensic psychiatric evaluation of persons with posttraumatic stress disorder undergoing criminal trial

The aim of our study was to determine if there is a difference between the type of crime committed by persons diagnosed with posttraumatic stress disorder (PTSD) and that committed by other offenders. The study included 389 male patients at the Department of Forensic Psychiatry in Popovaca who underwent forensic psychiatric evaluation to establish a psychiatric diagnosis, evaluate the mental capacity, and provide advice on further treatment. The data on the number of individuals with PTSD vs. other psychiatric disorders and the data on family violence vs. other criminal acts were analyzed with chi2 test. Of a total of 389 forensically evaluated male patients, 45 (11.6%) suffered from PTSD. Study subjects with PTSD only or PTSD comorbid with the other psychiatric disorders committed family violence significantly more often than subjects diagnosed with the other psychiatric disorders chi2(1) = 40.092, P < 0.001. Subjects with PTSD, whether or not comorbid with the other psychiatric disorders, committed family violence significantly more often than subjects with other psychiatric diagnoses.     

Automatic detection of voice impairments from text-dependent running speech

Acoustic analysis is a useful tool to diagnose voice diseases. Furthermore it presents several advantages: it is non-invasive, provides an objective diagnostic and, also, it can be used for the evaluation of surgical and pharmacological treatments and rehabilitation processes. Most of the approaches found in the literature address the automatic detection of voice impairments from speech by using the sustained phonation of vowels. In this paper it is proposed a new scheme for the detection of voice impairments from text-dependent running speech. The proposed methodology is based on the segmentation of speech into voiced and non-voiced frames, parameterising each voiced frame with mel-frequency cepstral parameters. The classification is carried out using a discriminative approach based on a multilayer perceptron neural network. The data used to train the system were taken from the voice disorders database distributed by Kay Elemetrics. The material used for training and testing contains the running speech corresponding to the well known “rainbow passage” of 140 patients (23 normal and 117 pathological). The results obtained are compared with those using sustained vowels. The text-dependent running speech showed a light improvement in the accuracy of the detection.     

Speech signal analysis of alzheimer’s diseases in farsi using auditory model system

In recent years, extensive studies have been conducted on the diagnosis of Alzheimer''s disease (AD) using the non-invasive speech signal recognition method. In this study, Farsi speech signals were analyzed using the auditory model system (AMS) in order to recognize AD. For this purpose, after the pre-processing of the speech signals and utilizing AMS, 4D outputs as function of time, frequency, rate, and scale range were obtained. The AMS outcomes were averaged in term of time to analyze the rate-frequency-scale for both groups, Alzheimer''s and healthy control subjects. Thereafter, the maximum of spectral and temporal modulation and frequency were extracted to classify by the support vector machine (SVM). The SVM achieves higher promising recognition accuracy with compare to prevalent approaches in the field of speech processing. The acceptable results demonstrate the applicability of the proposed algorithm in non-invasive and low-cost recognizing Alzheimer''s only using the few extracted features of the speech signal.     

Modeling cortisol dynamics in the neuro-endocrine axis distinguishes normal, depression, and post-traumatic stress disorder (PTSD) in humans

Cortisol, secreted in the adrenal cortex in response to stress, is an informative biomarker that distinguishes anxiety disorders such as major depression and post-traumatic stress disorder (PTSD) from normal subjects. Yehuda et al. proposed a hypothesis that, in humans, the hypersensitive hypothalamus-pituitary-adrenal (HPA) axis is responsible for the occurrence of differing levels of cortisol in anxiety disorders. Specifically, PTSD subjects have lower cortisol levels during the late subjective night in comparison to normal subjects, and this was assumed to occur due to strong negative feedback loops in the HPA axis. In the present work, to address this hypothesis, we modeled the cortisol dynamics using nonlinear ordinary differential equations and estimated the kinetic parameters of the model to fit the experimental data of three categories, namely, normal, depressed, and PTSD human subjects. We concatenated the subjects (n = 3) in each category and created a model subject (n = 1) without considering the patient-to-patient variability in each case. The parameters of the model for the three categories were simultaneously obtained through global optimization. Bifurcation analysis carried out with the optimized parameters exhibited two supercritical Hopf points and, for the choice of parameters, the oscillations were found to be circadian in nature. The fitted kinetic parameters indicate that PTSD subjects have a strong negative feedback loop and, as a result, the predicted oscillating cortisol levels are extremely low at the nadir in contrast to normal subjects, albeit within the endocrinologic range. We also simulated the phenotypes for each of the categories and, as observed in the clinical data of PTSD patients, the simulated cortisol levels are consistently low at the nadir, and correspondingly the negative feedback was found to be extremely strong. These results from the model support the hypothesis that high stress intensity and strong negative feedback loop may cause hypersensitive neuro-endocrine axis that results in hypocortisolemia in PTSD.     

Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach

Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologists in early diagnosis of mental disorders and preventing severe trauma. Major Depression Disorder (MDD) is a common and serious medical condition whose exact manifestations are not fully understood. So, early discovery of MDD patients helps to cure or limit the adverse effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as MDD due to having high temporal resolution information, and being a noninvasive, inexpensive and portable method. This paper has proposed an EEG-based deep learning framework that automatically discriminates MDD patients from healthy controls. First, the relationships among EEG channels in the form of effective brain connectivity analysis are extracted by Generalized Partial Directed Coherence (GPDC) and Direct directed transfer function (dDTF) methods. A novel combination of sixteen connectivity methods (GPDC and dDTF in eight frequency bands) was used to construct an image for each individual. Finally, the constructed images of EEG signals are applied to the five different deep learning architectures. The first and second algorithms were based on one and two-dimensional convolutional neural network (1DCNN–2DCNN). The third method is based on long short-term memory (LSTM) model, while the fourth and fifth algorithms utilized a combination of CNN with LSTM model namely, 1DCNN-LSTM and 2DCNN-LSTM. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals. The efficiency of the proposed algorithms is evaluated on resting state EEG data obtained from 30 healthy subjects and 34 MDD patients. The experiments show that the 1DCNN-LSTM applied on constructed image of effective connectivity achieves best results with accuracy of 99.24% due to specific architecture which captures the presence of spatial and temporal relations in the brain connectivity. The proposed method as a diagnostic tool is able to help clinicians for diagnosing the MDD patients for early diagnosis and treatment.     

A quality of life in chronic combat related posttraumatic stress disorder--a study on Croatian War veterans

The main objective of this study was to examine an association of various symptoms in chronic combat-related post traumatic stress disorder (PTSD) and the quality of life in this population. 248 Croatian male war veterans all diagnosed with chronic PTSD were consecutively enrolled in this study as they showed up at the routine check-up. They were given self report questionnaires Trauma Symptom Inventory (TSI-A) evaluating different PTSD symptoms and WHO Quality of Life-BREF assessing four different domains of the quality of life. After independent sample t- test was performed, the presence of each symptom defined by Trauma Symptom Inventory indicated the impairment of all four quality of life domains in a group of subject suffering from it, except of intrusive experience not being associated with the lesser quality in social domain. All quality of life domains were significantly correlated with various PTSD symptoms; however Pearson correlation factors ranged from small to medium value. As expected, PTSD symptoms are associated with lesser quality of life in the affected population. The further research is needed to show possible causal relationship between PTSD and, especially, physical health of these patients.     

SegNet and Salp Water Optimization-driven Deep Belief Network for Segmentation and Classification of Brain Tumor

Classification of brain tumor in Magnetic Resonance Imaging (MRI) images is highly popular in treatment planning, early diagnosis, and outcome evaluation. It is very difficult for classifying and diagnosing tumors from several images. Thus, an automatic prediction strategy is essential in classifying brain tumors as malignant, core, edema, or benign. In this research, a novel approach using Salp Water Optimization-based Deep Belief network (SWO-based DBN) is introduced to classify brain tumor. At the initial stage, the input image is pre-processed to eradicate the artifacts present in input image. Following pre-processing, the segmentation is executed by SegNet, where the SegNet is trained using the proposed SWO. Moreover, the Convolutional Neural Network (CNN) features are employed to mine the features for future processing. At last, the introduced SWO-based DBN technique efficiently categorizes the brain tumor with respect to the extracted features. Thereafter, the produced output of the introduced SegNet + SWO-based DBN is made use of in brain tumor segmentation and classification. The developed technique produced better results with highest values of accuracy at 0.933, specificity at 0.880, and sensitivity at 0.938 using BRATS, 2018 datasets and accuracy at 0.921, specificity at 0.853, and sensitivity at 0.928 for BRATS, 2020 dataset.     

Post‐traumatic stress disorder and beyond: an overview of rodent stress models

Post‐traumatic stress disorder (PTSD) is a psychiatric disorder of high prevalence and major socioeconomic impact. Patients suffering from PTSD typically present intrusion and avoidance symptoms and alterations in arousal, mood and cognition that last for more than 1 month. Animal models are an indispensable tool to investigate underlying pathophysiological pathways and, in particular, the complex interplay of neuroendocrine, genetic and environmental factors that may be responsible for PTSD induction. Since the 1960s, numerous stress paradigms in rodents have been developed, based largely on Seligman's seminal formulation of ‘learned helplessness’ in canines. Rodent stress models make use of physiological or psychological stressors such as foot shock, underwater trauma, social defeat, early life stress or predator‐based stress. Apart from the brief exposure to an acute stressor, chronic stress models combining a succession of different stressors for a period of several weeks have also been developed. Chronic stress models in rats and mice may elicit characteristic PTSD‐like symptoms alongside, more broadly, depressive‐like behaviours. In this review, the major existing rodent models of PTSD are reviewed in terms of validity, advantages and limitations; moreover, significant results and implications for future research—such as the role of FKBP5, a mediator of the glucocorticoid stress response and promising target for therapeutic interventions—are discussed.     

Serotonin and cortisol as suicidogenic factors in patients with PTSD

Post-traumatic stress disorder (PTSD) frequently occurs in commorbidity with different mental disorders, including suicidal behaviour. Group of biological factors, including serotonergic system, HPA axis and some genetic factors, are being studied as potential markers, able to differentiate suicidal and non-suicidal behaviour across the group of PTSD patients. This study is examining statistical relation between platelet serotonine concentration and serum cortisole concentration, within the group of PTSD patients with and without attempted suicide, treated at "Sveti Ivan" Psychiatric Hospital in Zagreb. The hypothesis of this study is that periferal biochemical markers are different across the groups of PTSD patients with and without attempted suicide and the group of healthy controls. Our results have shown significantly lower platelet serotonine concentration in PTSD patients with and without suicide behaviour, compared to healthy controls. There are no statistically significant differences of the serum cortisole concentration across observed groups. Our results correspond with those reported by other authors in this area of research, suggesting that platelet serotonine level might be used as potential periferal marker to detect risk of suicidal behaviour in PTSD patients.     

Classifier ensembles for protein structural class prediction with varying homology

Structural class characterizes the overall folding type of a protein or its domain. A number of computational methods have been proposed to predict structural class based on primary sequences; however, the accuracy of these methods is strongly affected by sequence homology. This paper proposes, an ensemble classification method and a compact feature-based sequence representation. This method improves prediction accuracy for the four main structural classes compared to competing methods, and provides highly accurate predictions for sequences of widely varying homologies. The experimental evaluation of the proposed method shows superior results across sequences that are characterized by entire homology spectrum, ranging from 25% to 90% homology. The error rates were reduced by over 20% when compared with using individual prediction methods and most commonly used composition vector representation of protein sequences. Comparisons with competing methods on three large benchmark datasets consistently show the superiority of the proposed method.     

Identification of protein coding regions of rice genes using alternative spectral rotation measure and linear discriminant analysis

An improved method, called Alternative Spectral Rotation (ASR) measure, for predicting protein coding regions in rice DNA has been developed. The method is based on the Spectral Rotation (SR) measure proposed by Kotlar and Lavner, and its accuracy is higher than that of the SR measure and the Spectral Content (SC) measure proposed by Tiwari et al. In order to increase the identifying accuracy,we chose three different coding characters, namely the asymmetric, purine, and stop-codon variables as parameters, and an approving result was presented by the method of Linear Discriminant Analysis (LDA).     

In Search of the Trauma Memory: A Meta-Analysis of Functional Neuroimaging Studies of Symptom Provocation in Posttraumatic Stress Disorder (PTSD)

Notwithstanding some discrepancy between results from neuroimaging studies of symptom provocation in posttraumatic stress disorder (PTSD), there is broad agreement as to the neural circuit underlying this disorder. It is thought to be characterized by an exaggerated amygdalar and decreased medial prefrontal activation to which the elevated anxiety state and concomitant inadequate emotional regulation are attributed. However, the proposed circuit falls short of accounting for the main symptom, unique among anxiety disorders to PTSD, namely, reexperiencing the precipitating event in the form of recurrent, distressing images and recollections. Owing to the technical demands, neuroimaging studies are usually carried out with small sample sizes. A meta-analysis of their findings is more likely to cast light on the involved cortical areas. Coordinate-based meta-analyses employing ES-SDM (Effect Size Signed Differential Mapping) were carried out on 19 studies with 274 PTSD patients. Thirteen of the studies included 145 trauma-exposed control participants. Comparisons between reactions to trauma-related stimuli and a control condition and group comparison of reactions to the trauma-related stimuli were submitted to meta-analysis. Compared to controls and the neutral condition, PTSD patients showed significant activation of the mid-line retrosplenial cortex and precuneus in response to trauma-related stimuli. These midline areas have been implicated in self-referential processing and salient autobiographical memory. PTSD patients also evidenced hyperactivation of the pregenual/anterior cingulate gyrus and bilateral amygdala to trauma-relevant, compared to neutral, stimuli. Patients showed significantly less activation than controls in sensory association areas such as the bilateral temporal gyri and extrastriate area which may indicate that the patients’ attention was diverted from the presented stimuli by being focused on the elicited trauma memory. Being involved in associative learning and priming, the retrosplenial cortex may have an important function in relation to trauma memory, in particular, the intrusive reexperiencing of the traumatic event.     

综述: 引发“战斗?鄄逃跑”反应的神经环路机制

"战斗-逃跑"反应是人和动物面临生存威胁时,产生的一系列应激行为和生理反应,该反应有助于个体提高战斗或逃跑的能力,以提高生存概率.过度或反复"战斗-逃跑"反应能诱发一类称为创伤后压力应激障碍(post-traumatic stress disorder,PTSD)的精神疾病.频繁的自然灾害和交通事故使我国人口受到PTSD的严重危害.要揭示PTSD的发病机理,首先需要深入地了解引发"战斗-逃跑"反应的脑内神经环路和机制.本文综述了该研究领域的进展和亮点工作,强调了该研究在国家相关领域中的重要性.     

Automatic recognition of bird individuals on an open set using as-is recordings

The most common method used to determine the identity of an individual bird is the capture-mark-recapture technique. The method has several major disadvantages, e.g. some species are difficult to capture/recapture and the capturing process itself may cause significant stress in animals leading even to injuries of more vulnerable species. Some studies introduce systems based on methods used for human identification. An automatic system for recognition of bird individuals (ASRBI) described in this article is based on a Gaussian mixture model (GMM) and a universal background model (GMM-UBM) method extended by an advanced voice activity detection (VAD) algorithm. It is focused on recognizing the bird individuals on an open set, i.e. any number of unknown birds may appear anytime during the identification process as is common in nature. The introduced ASRBI processes the recordings just as if they were recorded by an ornithologist: with durations from seconds to minutes, containing noise and unwanted sounds, as well as masking of the singer, etc. Thanks to the VAD algorithm, the proposed system is fully automatic, no manual pre-processing of recordings is needed, neither by cutting off the songs nor syllables. The overall achieved identification accuracy is 78.5%, the lowest 60.3% and the highest 95.7%. In total, 90% of all experiments reach at least 70% accuracy. The result suggests the application of the GMM-UBM with VAD is feasible for individual identification on the open set processing real-life recordings. The described method is capable of reducing both the time consumption and human intervention in animal monitoring projects.